llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Original model card

Breeze-7B is a language model family that builds on top of Mistral-7B, specifically intended for Traditional Chinese use.

Breeze-7B-Base is the base model for the Breeze-7B series. It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case.

Breeze-7B-Instruct derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.

Breeze-7B-Instruct-64k is a slightly modified version of Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters.

The current release version of Breeze-7B is v0.1.

Practicality-wise:

Breeze-7B-Base expands the original vocabulary with additional 30,000 Traditional Chinese tokens. With the expanded vocabulary, everything else being equal, Breeze-7B operates at twice the inference speed for Traditional Chinese to Mistral-7B and Llama 7B. [See Inference Performance.]
Breeze-7B-Instruct can be used as is for common tasks such as Q&A, RAG, multi-round chat, and summarization.
In particular, Breeze-7B-Instruct-64k can perform tasks at a document level, not a chapter level.

Performance-wise:

Breeze-7B-Instruct demonstrates impressive performance in benchmarks for Traditional Chinese, when compared to similar sized open-source contemporaries such as Taiwan-LLM-7B/13B-chat, QWen-7B-Chat, and Yi-6B-Chat. [See Chat Model Performance.]
Breeze-7B-Instruct shows comparable results to Mistral-7B-Instruct-v0.1 on the MMLU and MT-Bench benchmarks. [See Chat Model Performance.]

A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.

Features

Breeze-7B-Base-v0.1
- Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
- 8k-token context length
Breeze-7B-Instruct-v0.1
- Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
- 8k-token context length
- Multi-turn dialogue (without special handling for harmfulness)
Breeze-7B-Instruct-64k-v0.1
- Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
- 64k-token context length
- Multi-turn dialogue (without special handling for harmfulness)

Model Details

Breeze-7B-Base-v0.1
- Finetuned from: mistralai/Mistral-7B-v0.1
- Model type: Causal decoder-only transformer language model
- Language: English and Traditional Chinese (zh-tw)
Breeze-7B-Instruct-v0.1
- Finetuned from: MediaTek-Research/Breeze-7B-Base-v0.1
- Model type: Causal decoder-only transformer language model
- Language: English and Traditional Chinese (zh-tw)
Breeze-7B-Instruct-64k-v0.1
- Finetuned from: MediaTek-Research/Breeze-7B-Instruct-v0.1
- Model type: Causal decoder-only transformer language model
- Language: English and Traditional Chinese (zh-tw)

Base Model Performance

TMMLU+, DRCD, and Table source from MediaTek-Research/TCEval-v2. MediaTek-Research/TCEval-v2 derives from TCEval-v1 and ikala/tmmluplus. MMLU sources from hails/mmlu_no_train. We use the code revised from EleutherAI/lm-evaluation-harness to evaluate TMMLU+, DRCD, Table, and MMLU.

Models		↑ TMMLU+ (ACC)	DRCD (EM)	Table (ACC)	MMLU (ACC)
		TC, Knowledge	TC, Reasoning	TC, Reasoning	EN, Knowledge
		5 shot	3 shot	5 shot	5 shot
Yi-34B	34B	63.10	84.57	49.31	77.42
Qwen-14B	14B	51.30	16.95 *	50.69	68.83
Yi-6B	6B	49.63	76.61	34.72	65.35
Qwen-7B	7B	42.84	0.0 *	39.58	61.00
Breeze-7B-Base-v0.1	7B	40.35	81.13	28.47	61.63
Mistral-7B-v0.1	7B	36.93	79.27	27.78	64.89

* Few-shot learning cannot effectively guide the model to generate the proper answer.

Chat Model Performance

TMMLU+, DRCD, Table, and MT-Bench-tw source from MediaTek-Research/TCEval-v2. MediaTek-Research/TCEval-v2 derives from TCEval-v1 and ikala/tmmluplus. MMLU sources from hails/mmlu_no_train. MT-Bench source from lmsys/mt_bench_human_judgments. We use the code revised from EleutherAI/lm-evaluation-harness to evaluate TMMLU+, DRCD, Table, and MMLU. We use the code revised from fastchat llm_judge (GPT4 as judge) to evaluate MT-Bench-tw and MT-Bench.

Models		↑ MT-Bench-tw (Score)	TMMLU+ (ACC)	TMMLU+ (ACC)	DRCD (EM)	Table (ACC)	MT-Bench (Score)	MMLU (ACC)	MMLU (ACC)
		TC, Chat	TC, Knowledge	TC, Knowledge	TC, Reasoning	TC, Reasoning	EN, Chat	EN, Knowledge	EN, Knowledge
		0 shot	0 shot	5 shot	3 shot	0 shot	0 shot	0 shot	5 shot
gpt-3.5-turbo		7.1	41.76				7.9	70.00
Yi-34B-Chat	34B	6.9	54.87			36.81	7.6	71.04
Qwen-14B-Chat	14B	6.4	48.41			41.67	7.2	64.91
Breeze-7B-Instruct-v0.1	7B	5.7	41.61			45.83	7.1	63.26
Breeze-7B-Instruct-64k-v0.1	7B	5.5	40.99			36.11	7.1	63.68
Qwen-7B-Chat	7B	5.4	40.02			33.33	6.2	55.94
Yi-6B-Chat	6B	5.0	44.79			25.69	6.0	59.45
Taiwan-LLM-13B-v2.0-chat	13B	5.0	29.47			23.61	-*	50.50
Taiwan-LLM-7B-v2.1-chat	7B	4.2	28.08			31.25	-*	42.72

* Taiwan-LLM models responds to multi-turn questions (English) in Traditional Chinese.

Category Score of MT-Bench-tw (0 shot)

Models	STEM	Extraction	Reasoning	Math	Coding	Roleplay	Writing	Humanities	↑ AVG
gpt-3.5-turbo	7.8	6.1	5.1	6.4	6.2	8.7	7.4	9.3	7.1
Yi-34B-Chat	9.0	4.8	5.7	4.0	4.7	8.5	8.7	9.8	6.9
Qwen-14B-Chat	7.6	5.7	4.5	4.2	5.3	7.5	7.3	9.1	6.4
Breeze-7B-Instruct-v0.1	6.5	5.6	3.9	3.6	4.3	6.9	5.7	9.3	5.7
Breeze-7B-Instruct-64k-v0.1	6.1	5.3	3.7	2.9	4.2	7.0	6.7	8.3	5.5
Qwen-7B-Chat	6.6	4.5	4.8	2.9	3.6	6.2	6.8	8.2	5.4
Yi-6B-Chat	7.3	2.7	3.1	3.3	2.3	7.2	5.2	8.8	5.0
Taiwan-LLM-13B-v2.0-chat	6.1	3.4	4.1	2.3	3.1	7.4	6.6	6.8	5.0
Taiwan-LLM-7B-v2.1-chat	5.2	2.6	2.3	1.2	3.4	6.6	5.7	6.8	4.2

Category ACC of TMMLU+ (0 shot)

Model	STEM	Social Science	Humanities	Other	↑ AVG
Yi-34B-Chat	47.65	64.25	52.73	54.91	54.87
Qwen-14B-Chat	43.83	55.00	48.55	46.22	48.41
Yi-6B-Chat	37.80	51.74	45.36	44.25	44.79
gpt-3.5-turbo	41.56	46.72	36.73	42.03	41.76
Breeze-7B-Instruct-v0.1	37.41	46.81	42.06	40.16	41.61
Breeze-7B-Instruct-64k-v0.1	37.88	46.35	40.31	39.40	40.99
Qwen-7B-Chat	35.44	46.22	38.35	40.06	40.02
Taiwan-LLM-13B-v2.0-chat	27.74	33.69	27.03	29.43	29.47
Taiwan-LLM-7B-v2.1-chat	25.58	31.76	27.36	27.61	28.08

Inference Performance

In this test, we use the first 700 characters of the web article as the input and ask the model to write the same article again. All inferences run on 2 RTX A6000 GPUs (using vllm, with a tensor-parallel size of 2).

Models	↓ Inference Time (sec)	Estimated Max Input Length (Char)
Yi-6B	10.62	5.2k
Breeze-7B-Instruct-v0.1	10.74	11.1k
Breeze-7B-Instruct-64k-v0.1	10.74	88.8k
Qwen-7B	10.86	9.8k
Qwen-14B	18.89	9.8k
Mistral-7B-v0.1	20.48	5.1k
Taiwan-LLM-7B-v2.1-base	26.26	2.2k
Taiwan-LLM-13B-v2.0-base	36.80	2.2k
Yi-34B	43.71	4.5k

Long-context Performance

TBD

Examples

TBD

Use in Transformers

First install direct dependencies:

pip install transformers torch accelerate

If you want faster inference using flash-attention2, you need to install these dependencies:

bash

pip install packaging ninja
pip install flash-attn

Then load the model in transformers:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    model="MediaTek-Research/Breeze-7B-Instruct-v0.1",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    use_flash_attn_2=True # optional
)

The structure of the query template follows that of Mistral-7B-Instruct, as shown below.

<s> SYS_PROMPT [INST] QUERY1 [/INST] RESPONSE1 [INST] QUERY2 [/INST]

where SYS_PROMPT, QUERY1, RESPONSE1, and QUERY2 can be provided by the user.

The suggested default SYS_PROMPT is

You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.

Citation

@article{breeze7b2024,
  title={},
  author={},
  journal={arXiv},
  year={2024}
}

audreyt/Breeze-7B-Base-v0.1-GGUF

Breeze-7B-Base-v0.1-GGUF

Description

About GGUF